Cell Type Hierarchy Reconstruction via Reconciliation of Multi-resolution Cluster Tree

Abstract

A wealth of clustering algorithms are available for Single-cell RNA sequencing (scRNA-seq), but it remains challenging to compare and characterize the features across different scales of resolution. To resolve this challenge Multi-resolution Reconciled Tree (MRtree), builds a hierarchical tree structure based on multi-resolution partitions that is highly flexible and can be coupled with most scRNA-seq clustering algorithms. MRtree out-performs bottom-up or divisive hierarchical clustering approaches because it inherits the robustness and versatility of a flat clustering approach, while maintaining the hierarchical structure of cells. Application to fetal brain cells yields insight into subtypes of cells that can be reliably estimated.